Sciweavers

225 search results - page 31 / 45
» Spam, spam, spam, spam: how can we stop it
Sort
View
CEAS
2006
Springer
13 years 11 months ago
Fast Uncertainty Sampling for Labeling Large E-mail Corpora
One of the biggest challenges in building effective anti-spam solutions is designing systems to defend against the everevolving bag of tricks spammers use to defeat them. Because ...
Richard Segal, Ted Markowitz, William Arnold
CNIS
2006
13 years 9 months ago
Dynamically blocking access to web pages for spammers' harvesters
Almost all current anti spam measures are reactive, filtering being the most common. But to react means always to be one step behind. Reaction requires to predict the next action ...
Tobias Eggendorfer, Jörg Keller
KDD
2005
ACM
161views Data Mining» more  KDD 2005»
14 years 8 months ago
Combining email models for false positive reduction
Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolk...
Shlomo Hershkop, Salvatore J. Stolfo
AAAI
2012
11 years 10 months ago
Discovering Spammers in Social Networks
As the popularity of the social media increases, as evidenced in Twitter, Facebook and China’s Renren, spamming activities also picked up in numbers and variety. On social netwo...
Yin Zhu, Xiao Wang, ErHeng Zhong, Nathan Nan Liu, ...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 8 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley